alt Hacker News> LLMs aren't modeling "humans modeling the world" - they're modeling patterns in data that reflect the world directly.
This is a deranged and factually and tautologically (definitionally) false claim. LLMs can only work with tokenizations of texts written by people who produce those text to represent their actual models. All this removal and all these intermediate representational steps make LLMs a priori obviously even more distant from reality than humans. This is all definitional, what you are saying is just nonsense.
> When an LLM learns physics from textbooks, scientific papers, and code, it's learning the same compressed representations of reality that humans use, not a "model of a model."
A model is a compressed representation of reality. Physics is a model of the mechanics of various parts of the universe, i.e. "learning physics" is "learning a physical model". So, clarifying, the above sentence is
> When an LLM learns physical models from textbooks, scientific papers, and code, it's learning the model of reality that humans use, not a "model of a model."
This is clearly factually wrong, as the model that humans actually use is not the summaries written in textbooks, but the actual embodied and symbolic model that they use in reality, and which they only translate in corrupted and simplified, limited form to text (and that latter diminished form of all things is all the LLM can see). It is also not clear the LLM learns to actually do physics: it only learns how to write about physics like how humans do, but it doesn't mean it can run labs, interpret experiments, or apply models to novel contexts like humans can, or operate at the same level as humans. It clearly is learning something different from humans because it doesn't have the same sources of info.
> Your argument would suggest that because you learned about quantum mechanics through language (textbooks, lectures), you only have access to "humans' modeling of humans' modeling of quantum mechanics" - an infinite regress that's clearly absurd.
There is no infinite regress: humans actually verify that the things they learn and say are correct and provide effects, and update models accordingly. They do this by trying behaviours consistent with the learned model, and seeing how reality (other people, the physical world) responds (in degree and kind). LLMs have no conception of correctness or truth (not in any of the loss functions), and are trained and then done.
Humans can't learn solely from digesting texts either. Anyone who has done math knows that reading a textbook doesn't teach you almost anything, you have to actually solve the problems (and attempted-solving is not in much/any texts) and discuss your solutions and reasoning with others. Other domains involving embodied skills, like cooking, require other kinds of feedback from the environment and others. But LLMs are imprisoned in tokens.
EDIT: No serious researcher thinks LLMs are the way to AGI, this hasn't been a controversial opinion even among enthusiasts since about mid-2025 or so. This stuff about language is all trivial and basic stuff accepted by people in the field, and why things like V-JEPA-2 are being researched. So the comments here attempting to argue otherwise are really quite embarrassing.
>This is a deranged and factually and tautologically (definitionally) false claim.
Strong words for a weak argument. LLMs are trained on data generated by physical processes (keystrokes, sensors, cameras), not telepathically extracted "mental models." The text itself is the artifact of reality and not just a description of someone's internal state. If a sensor records the temperature and writes it to a log, is the log a "model of a model"? No, it’s a data trace of a physical reality.
>All this removal and all these intermediate representational steps make LLMs a priori obviously even more distant from reality than humans.
You're conflating mediation with distance. A photograph is "mediated" but can capture details invisible to human perception. Your eye mediates photons through biochemical cascades-equally "removed" from raw reality. Proximity isn't measured by steps in a causal chain.
>The model humans use is embodied, not the textbook summaries - LLMs only see the diminished form
You need to stop thinking that a textbook is a "corruption" of some pristine embodied understanding. Most human physics knowledge also comes from text, equations, and symbolic manipulation - not direct embodied experience with quantum fields. A physicist's understanding of QED is symbolic, not embodied. You've never felt a quark.
The "embodied" vs "symbolic" distinction doesn't privilege human learning the way you think. Most abstract human knowledge is also mediated through symbols.
>It's not clear LLMs learn to actually do physics - they just learn to write about it
This is testable and falsifiable - and increasingly falsified. LLMs:
Solve novel physics problems they've never seen
Debug code implementing physical simulations
Derive equations using valid mathematical reasoning
Make predictions that match experimental results
If they "only learn to write about physics," they shouldn't succeed at these tasks. The fact that they do suggests they've internalized the functional relationships, not just surface-level imitation.
>They can't run labs or interpret experiments like humans
Somewhat true. It's possible but they're not very good at it - but irrelevant to whether they learn physics models. A paralyzed theoretical physicist who's never run a lab still understands physics. The ability to physically manipulate equipment is orthogonal to understanding the mathematical structure of physical law. You're conflating "understanding physics" with "having a body that can do experimental physics" - those aren't the same thing.
>humans actually verify that the things they learn and say are correct and provide effects, and update models accordingly. They do this by trying behaviours consistent with the learned model, and seeing how reality (other people, the physical world) responds (in degree and kind). LLMs have no conception of correctness or truth (not in any of the loss functions), and are trained and then done.
Gradient descent is literally "trying behaviors consistent with the learned model and seeing how reality responds."
The model makes predictions
The Data provides feedback (the actual next token)
The model updates based on prediction error
This repeats billions of times
That's exactly the verify-update loop you describe for humans. The loss function explicitly encodes "correctness" as prediction accuracy against real data.
>No serious researcher thinks LLMs are the way to AGI... accepted by people in the field
Appeal to authority, also overstated. Plenty of researchers do think so and claiming consensus for your position is just false. LeCunn has been on that train for years so he's not an example of a change of heart. So far, nothing has actually come out of it. Even META isn't using V-JEPA to actually do anything, nevermind anyone else. Call me when these constructions actually best transformers.